class_docstr: Sequential User Matrix Model
  
  :Citation:
  
      Lian, J., Batal, I., Liu, Z., Soni, A., Kang, E. Y., Wang, Y., & Xie, X.,
      "Multi-Interest-Aware User Modeling for Large-Scale Sequential Recommendations", arXiv preprint arXiv:2102.09211, 2021.
docstr: Create a SUM cell
  
  Returns:
      object: An initialized SUM cell
docstr_tok: ["Create","a","SUM","cell","Returns",":","object",":","An","initialized","SUM","cell"]
code: def _create_sumcell(self):
      """Create a SUM cell
  
      Returns:
          object: An initialized SUM cell
      """
      hparams = self.hparams
      input_embedding_dim = self.history_embedding.shape[-1]
      input_params = [
          hparams.hidden_size * hparams.slots + input_embedding_dim,
          hparams.slots,
          hparams.attention_size,
          input_embedding_dim,
      ]
      sumcells = {"SUM": SUMCell, "SUMV2": SUMV2Cell}
      sumCell = sumcells[hparams.cell]
      res = None
      if hparams.cell in ["SUM", "SUMV2"]:
          res = sumCell(*input_params)
      else:
          raise ValueError(
              "ERROR! Cell type not support: {0}".format(hparams.cell)
          )
      return res
  
file_path: recommenders/recommenders/models/deeprec/models/sequential/sum.py
imports: import tensorflow
  from tensorflow.compat.v1.nn import dynamic_rnn
  from recommenders.models.deeprec.models.sequential.sequential_base_model import SequentialBaseModel
  from recommenders.models.deeprec.models.sequential.sum_cells import SUMCell, SUMV2Cell
label_desc: Use image node_id 3 for calling the SUMModel obj's underlying member method code with example usage: obj._create_sumcell() and returns: res